Abstract
The explosive growth of collected and stored data has generated a need for new techniques transforming these large amounts of data into useful comprehensible knowledge. Among these techniques, referred to as data mining, sequential pattern approaches handle sequence databases, extracting frequently occurring patterns related to time. Since most real-world databases consist of historical and quantitative data, some works have been done for mining the quantitative information stored within such sequence databases, uncovering fuzzy sequential patterns. In this chapter, we first introduce the various fuzzy sequential pattern approaches and the general principles they are based on. Then, we focus on a complete framework for mining fuzzy sequential patterns handling different levels of consideration of quantitative information. This framework is then applied to two real-life data sets: Web access logs and a textual database. We conclude on a discussion about future trends in fuzzy pattern mining.